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| Application of Machine Learning in Predicting Service Performance of Materials |
| Received:May 26, 2021 Revised:July 25, 2021 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/issn.1672-9242.2022.01.002 |
| KeyWord:data mining machine learning service performance materials engineering model prediction |
| Author | Institution |
| WANG Hong-ke |
Suzhou Nuclear Power Research Institute, Suzhou , China |
| LIU Xiao-tian |
Suzhou Nuclear Power Research Institute, Suzhou , China |
| LIN Lei |
Suzhou Nuclear Power Research Institute, Suzhou , China |
| SUN Hai-tao |
Nuclear and Radiation Safety Center, Beijing , China |
| LYU Yun-he |
Nuclear and Radiation Safety Center, Beijing , China |
| ZHANG Yan-wei |
Suzhou Nuclear Power Research Institute, Suzhou , China |
| XUE Fei |
Suzhou Nuclear Power Research Institute, Suzhou , China |
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| Abstract: |
| Aiming at the problems of large error, complex calculation and poor applicability in the prediction service performance of materials, machine learning (ML) based on data mining was proposed. Firstly, the application process of ML is elaborated. Then, the principle of common models and its application in material performance prediction are summarized. Then, various ML models were used to predict the irradiance properties of RPV steel. Furthermore, the prediction accuracy was improved by Stacking integration method. Results show that ML can be used to predict the service performance of materials with high accuracy and reliability. Appropriate models should be selected according to diverse characteristics of materials service data. Model fusion and parameters optimization can improve the prediction accuracy and calculation speed of the ML model effectively. |
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